timarni/dpo_it_hard_180

See axolotl config

axolotl version: 0.9.2

base_model: timarni/qwen3_dpo
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false

chat_template: qwen3
datasets:
  - path: timarni/MNLP_STEM_IT_HARD
    type: alpaca
    split: train

shuffle_merged_datasets: true

val_set_size: 0.1
output_dir: ./outputs/dpo_it_hard
dataset_prepared_path: last_run_prepared

sequence_len: 4096 #2048
sample_packing: true # was true -> need to check if it actually learns on the samples or not (better understand te hyperparam and event. install axolotl to debug)
eval_sample_packing: false
pad_to_sequence_len: true
# train_on_inputs: true # NEW
# group_by_length: false NEW?

# To be sure that no LORA is done
adapter: null
lora: false
merge_lora: false

wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_watch:
wandb_name: dpo_it_hard
wandb_log_model:

gradient_accumulation_steps: 16 # 2
micro_batch_size: 2 # 1
num_epochs: 15
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00001 # 0.00005
# cosine_min_lr_ratio: 0.1

warmup_ratio: 0.05
weight_decay: 0.01

bf16: auto
tf32: true

gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
gradient_clipping: 1.0 # or max_grad_norm?
flash_attention: true

evals_per_epoch: 2
saves_per_epoch: 1
save_total_limit: 20
special_tokens:

outputs/dpo_it_hard

This model is a fine-tuned version of timarni/qwen3_dpo on the timarni/MNLP_STEM_IT_HARD dataset. It achieves the following results on the evaluation set:

• Loss: 0.1297

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 16
• total_train_batch_size: 128
• total_eval_batch_size: 8
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 2
• num_epochs: 15.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.7556	0.3404	1	0.7317
0.7451	0.6809	2	0.5623
0.5054	1.0	3	0.2737
0.1901	1.3404	4	0.1879
0.1304	1.6809	5	0.1532
0.1146	2.0	6	0.1421
0.1046	2.3404	7	0.1377
0.1001	2.6809	8	0.1353
0.1009	3.0	9	0.1338
0.0957	3.3404	10	0.1330
0.0931	3.6809	11	0.1323
0.0945	4.0	12	0.1316
0.0914	4.3404	13	0.1312
0.0894	4.6809	14	0.1307
0.0912	5.0	15	0.1303
0.0883	5.3404	16	0.1302
0.0868	5.6809	17	0.1301
0.0889	6.0	18	0.1299
0.0864	6.3404	19	0.1299
0.0856	6.6809	20	0.1298
0.0878	7.0	21	0.1299
0.0858	7.3404	22	0.1299
0.085	7.6809	23	0.1298
0.0874	8.0	24	0.1298
0.0855	8.3404	25	0.1299
0.0849	8.6809	26	0.1297
0.0873	9.0	27	0.1298
0.0854	9.3404	28	0.1297
0.0849	9.6809	29	0.1297
0.0873	10.0	30	0.1297

Framework versions

• Transformers 4.51.3
• Pytorch 2.5.1+cu121
• Datasets 3.5.1
• Tokenizers 0.21.1